What is an AI Engineer at Ramp?

At Ramp, the role of an AI Engineer is not about theoretical research or training massive foundation models in a vacuum. It is about application, velocity, and tangible business impact. You are joining a team that believes AI is the new foundation for how business gets done. Whether you are in the Applied AI engineering track or the AI Operations/Enablement track, your goal is the same: to leverage Large Language Models (LLMs) and automation to save companies time and money, and to make Ramp the most productive company in the world.

In this role, you will work on the cutting edge of financial technology. You might be building AI Agents that autonomously handle procurement, designing Retrieval-Augmented Generation (RAG) systems to answer complex policy questions, or developing structured extraction tools (like Ramp's open-source jsonformer) to turn messy documents into clean data. Alternatively, you may be focused on internal enablement, deploying workflows using tools like Gumloop and n8n to supercharge the productivity of Ramp's non-engineering teams.

This position is critical because Ramp operates at a massive scale, processing billions in transaction volume. You are expected to be a "vibe coder," a product owner, and a technical expert all in one. You will ship full-stack projects end-to-end, often moving from idea to production in days, not months. If you are bold enough to build the future of finance and prefer hands-on delivery over strategy briefings, this is the environment for you.

Common Interview Questions

Getting Ready for Your Interviews

Preparing for an interview at Ramp requires a shift in mindset. You need to demonstrate not just that you can code, but that you can build useful products quickly and pragmatically. The process is competitive and often automated in the early stages to filter for high technical competence.

Key Evaluation Criteria:

• Applied Technical Fluency – You must demonstrate the ability to use AI tools (LLMs, APIs, Vector DBs) and standard engineering stacks (Python, Typescript, SQL) to solve real problems. We look for builders who understand the limitations of current models and how to engineer around them.
• Operational Velocity – Ramp is famous for its speed. Interviewers assess whether you can ship high-quality work fast. Perfectionism that hinders progress is viewed negatively; pragmatic, rapid iteration is valued.
• Product & Operational Empathy – particularly for the Enablement roles, you need to understand the "business" side. Can you map a chaotic human process and automate it? Do you understand the user's pain point?
• Problem Solving under Ambiguity – You will often be given open-ended problems (e.g., "How would you automate invoice processing?"). You need to structure your answer logically, defining the inputs, the processing logic, and the outputs clearly.

Interview Process Overview

The interview process for AI Engineering roles at Ramp is rigorous, efficient, and heavily focused on technical execution. Candidates should expect a process that moves quickly but demands a high standard of performance right from the start. Unlike traditional big tech companies that may focus heavily on whiteboard theory, Ramp focuses on your ability to use tools to build solutions.

The process typically begins with an Online Assessment (OA) or a one-way video interview. Recent candidates report that the coding challenges are often hosted on platforms like HackerRank and can range from Medium to Hard difficulty. Note that the screening is strict; even a perfect score on the coding assessment does not guarantee a next round if your resume or project portfolio does not align perfectly with the team's immediate needs.

If you pass the initial automated screens, you will move to technical deep dives. These may involve live coding sessions, system design discussions focused on AI infrastructure (e.g., RAG, inference), or practical automation challenges. The final stages involve meeting with engineering leaders and potential teammates to assess "Ramp speed" and cultural alignment. The entire loop is designed to identify self-starters who can operate autonomously.

Understanding the Timeline: The visual timeline above illustrates the typical flow from application to offer. Note the emphasis on the initial "Screening & Assessment" phase; this is where the highest volume of candidates are filtered out, often via automated coding tests or one-way video responses. Candidates should treat these asynchronous steps with the same seriousness as a live interview.

Deep Dive into Evaluation Areas

Ramp evaluates AI Engineers on a mix of core software engineering fundamentals and specific applied AI capabilities. Because the team is lean and moves fast, there is little room for "learning on the job" regarding the fundamentals.

Coding & Algorithms

Despite being an AI role, strong foundational coding skills are non-negotiable. You will face automated assessments that test your ability to write clean, efficient code under time pressure.

• Data Structures: Expect questions involving arrays, hashmaps, and trees.
• Complexity: You must be able to analyze Time and Space complexity.
• Practical Scripting: For Ops/Enablement roles, you may be tested on your ability to write scripts to glue APIs together.

Applied AI & LLM Systems

This is the core of the role. You need to know how to take an off-the-shelf model and make it work for production use cases.

• RAG (Retrieval-Augmented Generation): Understanding how to chunk data, generate embeddings, and retrieve relevant context for an LLM.
• Structured Output: How to force an LLM to output valid JSON or SQL (a key focus for Ramp).
• Prompt Engineering & Chaining: Experience with tools like LangChain or building custom chains to handle complex logic.
• Agents: Designing systems where the AI can take actions (e.g., browsing the web, querying a database) rather than just answering questions.

System Design & Infrastructure

For the "Applied AI Engineer" track, you must understand the backend systems that support AI.

• Inference Architecture: How to serve models with low latency.
• Integration: Connecting AI services to existing web frameworks and databases.
• Scalability: Handling rate limits, cost management for API calls, and queuing systems.

Operational Automation (Enablement Focus)

For "AI Operations" roles, the evaluation shifts toward process logic and low-code tools.

• Workflow Orchestration: Using tools like n8n or Gumloop to build complex logic flows.
• Process Mapping: Taking a vague business requirement (e.g., "Automate support tickets") and breaking it down into a step-by-step algorithmic workflow.

Be ready to go over:

• Structured Extraction: Techniques for getting reliable data out of unstructured text (PDFs, invoices).
• Evaluation: How do you measure if your AI feature is actually working? (Evals, Golden Datasets).
• Tool Usage: Specific experience with Cursor, Claude Code, or OpenAI APIs.
• Advanced concepts: Fine-tuning models vs. few-shot prompting; Vector database selection (Pinecone, Milvus, etc.).

Example questions or scenarios:

• "Given a raw invoice PDF, how would you design a pipeline to extract the Vendor Name, Date, and Total Amount with 100% schema compliance?"
• "We want to build a chatbot that answers employee questions about HR benefits. How do you architect this so it doesn't hallucinate?"
• "Write a function to traverse a dependency graph of tasks." (Standard coding question).